%0期刊文章%@ 2561-326X %I JMIR出版物%V 6% 卡塔尔世界杯8强波胆分析N 5% P e36238% T COVID-19接触者追踪应用程序的全球用户级感知:使用自然语言处理的数据驱动方法艾哈迈德，卡希夫，菲罗杰，卡迪尔，朱aid, Qolomany，巴希尔，伊姆兰，塔尔哈特，苏莱曼，穆罕默德，奈娜，哈桑，Syed zohab，古尔，阿斯玛，阿斯豪斯，莫瓦法，阿尔-福卡哈，阿拉+哈马德·本·哈利法大学科学与工程学院信息与计算技术部，教育城，卡塔尔多哈，974 50694322，aalfuqaha@hbku.edu.qa %K COVID-19 %K情感分析%K接触追踪应用程序%K NLP %K文本分类%K BERT %K fastText %K变压器%K RoBerta %D 2022 %7 11.5.2022 %9原始论文%J JMIR表单Res %G英文%X背景:接触追踪已在全球范围内采用，以控制COVID-19的感染率。为了实现这一目标，一些移动应用程序已经被开发出来。然而，人们越来越关注这些应用程序的工作机制和性能。文献已经提供了一些有趣的探索性研究，通过分析来自不同来源的信息，如新闻和用户对应用程序的评论，来研究社区对应用程序的反应。然而，据我们所知，目前还没有一种解决方案可以自动分析用户的评论并提取所引发的情绪。我们相信，这样的解决方案与用户友好的界面相结合，可以作为一种快速监控工具来监控应用程序的有效性，并在不经过密集的参与式设计方法的情况下立即进行更改。目的:本文旨在分析人工智能和NLP技术在自动提取和分类用户情绪极性方面的效果，提出一个情绪分析框架，自动分析用户对COVID-19接触者追踪手机应用程序的评论。我们还旨在提供一个大规模的带注释的基准数据集，以促进该领域未来的研究。 As a proof of concept, we also developed a web application based on the proposed solutions, which is expected to help the community quickly analyze the potential of an application in the domain. Methods: We propose a pipeline starting from manual annotation via a crowd-sourcing study and concluding with the development and training of artificial intelligence (AI) models for automatic sentiment analysis of users’ reviews. In detail, we collected and annotated a large-scale data set of user reviews on COVID-19 contact tracing applications. We used both classical and deep learning methods for classification experiments. Results: We used 8 different methods on 3 different tasks, achieving up to an average F1 score of 94.8%, indicating the feasibility of the proposed solution. The crowd-sourcing activity resulted in a large-scale benchmark data set composed of 34,534 manually annotated reviews. Conclusions: The existing literature mostly relies on the manual or exploratory analysis of users’ reviews on applications, which is tedious and time-consuming. In existing studies, generally, data from fewer applications are analyzed. In this work, we showed that AI and natural language processing techniques provide good results for analyzing and classifying users’ sentiments’ polarity and that automatic sentiment analysis can help to analyze users’ responses more accurately and quickly. We also provided a large-scale benchmark data set. We believe the presented analysis, data set, and proposed solutions combined with a user-friendly interface can be used as a rapid surveillance tool to analyze and monitor mobile apps deployed in emergency situations leading to rapid changes in the applications without going through an intense participatory design method. %M 35389357 %R 10.2196/36238 %U https://formative.www.mybigtv.com/2022/5/e36238 %U https://doi.org/10.2196/36238 %U http://www.ncbi.nlm.nih.gov/pubmed/35389357